Comparison of deep-learning and conventional machine learning algorithms for salary prediction

Research Article
Open access

Comparison of deep-learning and conventional machine learning algorithms for salary prediction

Ziyuan Feng 1 , Zixian Liu 2 , Yibo Yin 3*
  • 1 School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China    
  • 2 College of Engineering, Hong Kong Polytechnic University, HongKong, 999077, China    
  • 3 School of Economics and Management, Tongji University, Shanghai, 200092, China    
  • *corresponding author dolado@tongji.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230910
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Salary is an integral part of contemporary life. With the large-scale use of machine learning, it has become possible to predict salaries with machine learning. Previous researchers have used random forest algorithms to solve this problem, however, there is a research gap in using a neural network to solve this problem. Therefore, the research topic of this paper is to use convolutional neural networks (CNN) and datasets on Kaggle to predict salary. The research methodology of this paper is as follows. First, the Kaggle dataset is divided into the train-dataset and the test-dataset. After preprocessing the data, two kinds of features are obtained. The features will be transformed into two two-dimensional matrices. Next, the matrices were used to train two CNNs separately. These two CNNs will be connected together to get the predicted salary by fully connected layers and Relu activation functions. After training the CNN, the study called the test dataset to verify the accuracy of the model. Similarly, the study used a random forest model for prediction. Finally, the comparison of the two results showed which algorithm was better. The study found that the error rate of the CNN was 0.0732 and its variance was 0.1899. The error rate of the random forest was 0.2437 and its variance was 0.8285. From the results, CNN is better than random forest in terms of accuracy and stability. Therefore, using CNN for salary prediction has a high probability of getting better results.

Keywords:

salary prediction, deep learning, convolutional neural network.

Feng,Z.;Liu,Z.;Yin,Y. (2023). Comparison of deep-learning and conventional machine learning algorithms for salary prediction. Applied and Computational Engineering,6,643-651.
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References

[1]. Khongchai, P., & Songmuang, P. (2016). Implement of salary prediction system to improve student motivation using data mining technique. In 2016 11th International Conference on Knowledge, Information and Creativity Support Systems, 1-6.

[2]. Hu, G., Yang, Y., Yi, D., Kittler, J., Christmas, W., Li, S. Z., & Hospedales, T. (2015). When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. In Proceedings of the IEEE international conference on computer vision workshops, 142-150.

[3]. Bahar, P., Bieschke, T., & Ney, H. (2019). A comparative study on end-to-end speech to text translation. In 2019 IEEE Automatic Speech Recognition and Understanding Workshop, 792-799.

[4]. Li, Y., & Shahabi, C. (2018). A brief overview of machine learning methods for short-term traffic forecasting and future directions. Sigspatial Special, 10(1), 3-9.

[5]. Shahbazi, Z., & Byun, Y. C. (2021). Improving the product recommendation system based-on customer interest for online shopping using deep reinforcement learning. Soft Computing and Machine Intelligence, 1(1), 31-35.

[6]. Stilgoe, J. (2018). Machine learning, social learning and the governance of self-driving cars. Social studies of science, 48(1), 25-56.

[7]. Khongchai, P., & Songmuang, P. (2016). Random forest for salary prediction system to improve students' motivation. In 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems, 637-642.

[8]. Dutta, S., Halder, A., & Dasgupta, K. (2018). Design of a novel prediction engine for predicting suitable salary for a job. In 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks, 275-279.

[9]. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., et al. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377.

[10]. Adzuna (2013) Job Salary Prediction. URL: https://www.kaggle.com/competitions/job-salary-prediction/overview/description

Cite this article

Feng,Z.;Liu,Z.;Yin,Y. (2023). Comparison of deep-learning and conventional machine learning algorithms for salary prediction. Applied and Computational Engineering,6,643-651.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Khongchai, P., & Songmuang, P. (2016). Implement of salary prediction system to improve student motivation using data mining technique. In 2016 11th International Conference on Knowledge, Information and Creativity Support Systems, 1-6.

[2]. Hu, G., Yang, Y., Yi, D., Kittler, J., Christmas, W., Li, S. Z., & Hospedales, T. (2015). When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. In Proceedings of the IEEE international conference on computer vision workshops, 142-150.

[3]. Bahar, P., Bieschke, T., & Ney, H. (2019). A comparative study on end-to-end speech to text translation. In 2019 IEEE Automatic Speech Recognition and Understanding Workshop, 792-799.

[4]. Li, Y., & Shahabi, C. (2018). A brief overview of machine learning methods for short-term traffic forecasting and future directions. Sigspatial Special, 10(1), 3-9.

[5]. Shahbazi, Z., & Byun, Y. C. (2021). Improving the product recommendation system based-on customer interest for online shopping using deep reinforcement learning. Soft Computing and Machine Intelligence, 1(1), 31-35.

[6]. Stilgoe, J. (2018). Machine learning, social learning and the governance of self-driving cars. Social studies of science, 48(1), 25-56.

[7]. Khongchai, P., & Songmuang, P. (2016). Random forest for salary prediction system to improve students' motivation. In 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems, 637-642.

[8]. Dutta, S., Halder, A., & Dasgupta, K. (2018). Design of a novel prediction engine for predicting suitable salary for a job. In 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks, 275-279.

[9]. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., et al. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377.

[10]. Adzuna (2013) Job Salary Prediction. URL: https://www.kaggle.com/competitions/job-salary-prediction/overview/description

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